This code provides a hyper-parameter optimization implementation for machine learning algorithms, as described in the paper: L. Yang and A. Shami, “On hyperparameter optimization of machine learning ...

Abstract: This paper investigates the significance of hyperparameter optimization in meta-learning for image classification tasks. Despite advancements in deep learning, real-time image classification ...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...

Impact Statement: Hyperparameter tuning is critical for enhancing model performance but poses challenges in high-dimensional spaces. Existing gradient-based methods approximate the hypergradient ...

No matter the strength of a model's architecture or the quality of its training data, it's unlikely to perform optimally without the right hyperparameter values. Hyperparameters play a key role in ...

Install pytorch and torchvision, for questions first refer to https://pytorch.org/docs/stable/index.html. Preliminary: read MNIST example https://github.com/pytorch ...

The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tackle classically ...

Hyper-parameters are parameters used to regulate how the algorithm behaves while it creates the model. These factors cannot be discovered by routine training. Before the model is trained, it must be ...